"A lot of people in academia are not very good at software engineering," says Kenny Daniel, co-founder and chief technology officer of cloud computing startup Algorithmia. "I always had more of the software engineering bent."
That, in a nutshell, is some of what makes six-year-old, Seattle-based Algorithmia uniquely focused in a world over-run with machine learning offerings.
Amazon, Microsoft, Google, IBM, Salesforce, and other large companies have for some time been offering cut-and-paste machine learning in their cloud services. Why would you want to stray to a small, young company?
No reason, unless that startup had a particular knack for hands-on support of machine learning.
That's the premise of Daniel's firm, founded with Diego Oppenheimer, a graduate of Carnegie Mellon and a veteran of Microsoft. The two became best friends in undergrad at CMU, and when Oppenheimer went to industry, Daniel went to pursue a PhD in machine learning at USC. While researching ML, Daniel realized he wanted to build things more than he wanted to just theorize.
"I had the idea for Algorithmia in grad school," Daniel recalled in an interview with ZDNet. "I saw the struggle of getting the work out into the real world; my colleagues and I were developing state-of-the-art [machine learning] models, but not really getting them adopted in the real world the way we wanted."
He dropped out of USC and hooked up with Oppenheimer to found the company. Oppenheimer had seen from the industry side that even for large companies such as Microsoft, there was a struggle to get enough talent to get things deployed and in production.
The duo initially set out to create an App Store for machine learning, a marketplace in which people could buy and sell ML models, or programs. They got seed funding from venture firm Madrona Ventures, and took up residence in Seattle's Pike Place. "There's a tremendous amount of ML talent out here, and the rents are not as crazy" as Silicon Valley, he explained.
"If companies are not getting the pay-off, if there's a lack of progress, we could be looking at another hype cycle," says Kenny Daniel, CTO and co-founder of machine learning operations service provider Algorithmia.
Their intent was to match up consumers of machine learning, companies that wanted the models, with developers. But Daniel noticed something was breaking down. The majority of customers using the service were consuming machine learning from their own teams. There was little transaction volume because companies were just trying to get stuff to work.
"We said, okay, there's something else going on here: people don't have a great way of turning their models into scalable, production-ready APIs that are highly available and resilient," he recalled having realized.
"A lot of these companies would have data scientists building models in Jupyter on their laptop, and not really having a good way to hook them up to a million iOS apps that are trying to recognize images, or a back-end data pipeline that's trying to process terabytes of data a day."
There was, in other words, "a gap there in software engineering." And so the business shifted from a focus on a marketplace to a focus on providing the infrastructure to make customers' machine learning models scale up.
The company had to solve a lot of the multi-tenant challenges that were fundamental limitations, long before those techniques became mainstream with the big cloud platforms.
Also: How do we know AI is ready to be in the wild? Maybe a critic is needed
"We were running functions before AWS Lambda," says Daniel, referring to Amazon's server-less offering.
Problems such as, "How do you manage GPUs, because GPUs were not built for this kind of thing, they were built to make games run fast, not for multi-tenant users to run jobs on them."
Daniel and Oppenheimer started meeting with big financial and insurance firms, to discuss solving their deployment problems. Training a machine learning model might be fine on AWS. But when it came time to make predictions with the trained model, to put it into production for a high volume of requests, companies were running into issues.
The companies wanted their own instances of their machine learning models in virtual private clouds, on AWS or Azure, with the ability to have dedicated customer support, metrics, management and monitoring.
That lead to the creation of an Algorithmia Enterprise service in 2016. That was made possible by fresh capital, an infusion of $10.5 million from Gradient Ventures, Google's AI investment operation, followed by a $25 million round last summer. In total. Algorithmia has received $37.9 million in funding.
Today, the company has seven-figure deals with large institutions, most of it for running private deployments. You could get something like what Algorithmia offers by using Amazon's SageMaker, for example. But SageMaker is all about using only Amazon's resources. The appeal with Algorithmia is that the deployments will run in multiple cloud facilities, wherever a customer needs machine learning to live.
"A number of these institutions need to have parity across wherever their data is," said Daniel. "You may have data on premise, or maybe you did acquisitions, and things are across multiple clouds; being able to have parity across those is one of the reasons people choose Algorithmia."
Amazon and other cloud giants each tout their offerings as end-to-end services, said Daniel. But that runs counter to reality, which is that there is a soup composed of many technologies that need to be brought together to make ML work.
"In the history of software, there hasn't been a clear end-to-end, be-all winner," Daniel observed. "That's why GitHub, and GitLab, and Bitbucket and all these continue to exist, and there are different CI [continuous integration] systems, and Jenkins, and different deployment systems and different container systems."
"It takes a fair amount of expertise to wire all these things together."
There is some independent support for what Daniel claims. Gartner analyst Arun Chandrasekaran puts Algorithmia in a basket that he calls "ModelOps." The application "life cycle" of artificial intelligence programs,
Chandrasekaran told ZDNet, is different from that of traditional applications, "due to the sheer complexity and dynamism of the environment."
"Most organizations underestimate how long it will take to move AI and ML projects into production."
Also: Recipe for selling software in a pandemic: Be essential, add some machine learning, and focus, focus, focus
Chandrasekaran predicts the market for ModelOps will expand as more and more companies try to deploy AI and run up against the practical hurdles.
While there is the risk that cloud operators will subsume some of what Algorithmia offers, said Chandrasekaran, the need to deploy outside a single cloud supports the role of independent ModelOps vendors such as Algorithmia.
"AI deployments tend to be hybrid, both from the perspective of spanning multiple environments (on-premises, cloud) as well as the different AI techniques that customers may use," he told ZDNet.
Aside from cloud vendors, Algorithmia competitors include Datarobot, H20.ai, RapidMiner, Hydrosphere, Modelop and Seldon.
Some companies may go 100% AWS, conceded Daniel. And some customers may be fine with generic abilities of cloud vendors. For example, Amazon has made a lot of progress with text translation technology as a service, he noted.
But industry-specific, or vertical market machine learning, is something of a different story. One customer of Algorithmia, a large financial firm, needed to deploy an application for fraud detection. "It sounds crazy, but we had to figure out all this stuff of, how do we know this data over here is used to train this model? It's important because its an issue of their [the client's] liability."
The immediate priority for Algorithmia is a new product version called Teams that lets companies organize an invite-only, hosted gathering of those working on a particular model. It can stretch across multiple "federated" instances of a model, said Daniel. The pricing is by compute usage, so it's a pay-as-you-go option, versus the annual billing of the Enterprise version.
Also: AI startup Abacus goes live with commercial deep learning service, takes $13M Series A financing
To Daniel, the gulf that he observed in academia between pure research and software engineering is the thing that has always shot down AI in past. The so-called "AI winter" periods over the decades were in large part a result of the practical obstacles, he believes.
"Those were periods when there was hype for AI and ML, and companies invested a lot of money," he said. "If companies are not getting the pay-off, if there's a lack of progress, we could be looking at another hype cycle."
By contrast, if more companies can be successful in deployment, it may lead to a flourishing of the kind of marketplace that he and Oppenheimer originally envisioned.
"It's like the Unix philosophy, these small things combining, that's the way that I see it," he said. "Ultimately, this will just enable all sorts of things, completely new scenarios, and that's incredibly valuable, things that we can make available in a free market of machine learning."
Read more here:
- Classic reasoning systems like Loom and PowerLoom vs. more modern systems based on probalistic networks - November 8th, 2009 [November 8th, 2009]
- Using Amazon's cloud service for computationally expensive calculations - November 8th, 2009 [November 8th, 2009]
- Software environments for working on AI projects - November 8th, 2009 [November 8th, 2009]
- New version of my NLP toolkit - November 8th, 2009 [November 8th, 2009]
- Semantic Web: through the back door with HTML and CSS - November 8th, 2009 [November 8th, 2009]
- Java FastTag part of speech tagger is now released under the LGPL - November 8th, 2009 [November 8th, 2009]
- Defining AI and Knowledge Engineering - November 8th, 2009 [November 8th, 2009]
- Great Overview of Knowledge Representation - November 8th, 2009 [November 8th, 2009]
- Something like Google page rank for semantic web URIs - November 8th, 2009 [November 8th, 2009]
- My experiences writing AI software for vehicle control in games and virtual reality systems - November 8th, 2009 [November 8th, 2009]
- The URL for this blog has changed - November 8th, 2009 [November 8th, 2009]
- I have a new page on Knowledge Management - November 8th, 2009 [November 8th, 2009]
- N-GRAM analysis using Ruby - November 8th, 2009 [November 8th, 2009]
- Good video: Knowledge Representation and the Semantic Web - November 8th, 2009 [November 8th, 2009]
- Using the PowerLoom reasoning system with JRuby - November 8th, 2009 [November 8th, 2009]
- Machines Like Us - November 8th, 2009 [November 8th, 2009]
- RapidMiner machine learning, data mining, and visualization tool - November 8th, 2009 [November 8th, 2009]
- texai.org - November 8th, 2009 [November 8th, 2009]
- NLTK: The Natural Language Toolkit - November 8th, 2009 [November 8th, 2009]
- My OpenCalais Ruby client library - November 8th, 2009 [November 8th, 2009]
- Ruby API for accessing Freebase/Metaweb structured data - November 8th, 2009 [November 8th, 2009]
- Protégé OWL Ontology Editor - November 8th, 2009 [November 8th, 2009]
- New version of Numenta software is available - November 8th, 2009 [November 8th, 2009]
- Very nice: Elsevier IJCAI AI Journal articles now available for free as PDFs - November 8th, 2009 [November 8th, 2009]
- Verison 2.0 of OpenCyc is available - November 8th, 2009 [November 8th, 2009]
- What’s Your Biggest Question about Artificial Intelligence? [Article] - November 8th, 2009 [November 8th, 2009]
- Minimax Search [Knowledge] - November 8th, 2009 [November 8th, 2009]
- Decision Tree [Knowledge] - November 8th, 2009 [November 8th, 2009]
- More AI Content & Format Preference Poll [Article] - November 8th, 2009 [November 8th, 2009]
- New Planners Solve Rescue Missions [News] - November 8th, 2009 [November 8th, 2009]
- Neural Network Learns to Bluff at Poker [News] - November 8th, 2009 [November 8th, 2009]
- Pushing the Limits of Game AI Technology [News] - November 8th, 2009 [November 8th, 2009]
- Mining Data for the Netflix Prize [News] - November 8th, 2009 [November 8th, 2009]
- Interview with Peter Denning on the Principles of Computing [News] - November 8th, 2009 [November 8th, 2009]
- Decision Making for Medical Support [News] - November 8th, 2009 [November 8th, 2009]
- Neural Network Creates Music CD [News] - November 8th, 2009 [November 8th, 2009]
- jKilavuz - a guide in the polygon soup [News] - November 8th, 2009 [November 8th, 2009]
- Artificial General Intelligence: Now Is the Time [News] - November 8th, 2009 [November 8th, 2009]
- Apply AI 2007 Roundtable Report [News] - November 8th, 2009 [November 8th, 2009]
- What Would You do With 80 Cores? [News] - November 8th, 2009 [November 8th, 2009]
- Software Finds Learning Language Child's Play [News] - November 8th, 2009 [November 8th, 2009]
- Artificial Intelligence in Games [Article] - November 8th, 2009 [November 8th, 2009]
- Artificial Intelligence Resources - November 8th, 2009 [November 8th, 2009]
- Alan Turing: Mathematical Biologist? - April 25th, 2012 [April 25th, 2012]
- BBC Horizon: The Hunt for AI ( Artificial Intelligence ) - Video - April 30th, 2012 [April 30th, 2012]
- Can computers have true artificial intelligence" Masonic handshake" 3rd-April-2012 - Video - April 30th, 2012 [April 30th, 2012]
- Kevin B. Korb - Interview - Artificial Intelligence and the Singularity p3 - Video - April 30th, 2012 [April 30th, 2012]
- Artificial Intelligence - 6 Month Anniversary - Video - April 30th, 2012 [April 30th, 2012]
- Science Breakthroughs - April 30th, 2012 [April 30th, 2012]
- Hitman: Blood Money - Part 49 - Stupid Artificial Intelligence! - Video - April 30th, 2012 [April 30th, 2012]
- Research Members Turned Off By HAARP Artificial Intelligence - Video - April 30th, 2012 [April 30th, 2012]
- Artificial Intelligence Lecture No. 5 - Video - April 30th, 2012 [April 30th, 2012]
- The Artificial Intelligence Laboratory, 2012 - Video - April 30th, 2012 [April 30th, 2012]
- Charlie Rose - Artificial Intelligence - Video - April 30th, 2012 [April 30th, 2012]
- Expert on artificial intelligence to speak at EPIIC Nights dinner - May 4th, 2012 [May 4th, 2012]
- Filipino software engineers complete and best thousands on Stanford’s Artificial Intelligence Course - May 4th, 2012 [May 4th, 2012]
- Vodafone xone™ Hackathon Challenges Developers and Entrepreneurs to Build a New Generation of Artificial Intelligence ... - May 4th, 2012 [May 4th, 2012]
- Rocket Fuel Packages Up CPG Booster - May 4th, 2012 [May 4th, 2012]
- 2 Filipinos finishes among top in Stanford’s Artificial Intelligence course - May 5th, 2012 [May 5th, 2012]
- Why Your Brain Isn't A Computer - May 5th, 2012 [May 5th, 2012]
- 2 Pinoy software engineers complete Stanford's AI course - May 7th, 2012 [May 7th, 2012]
- Percipio Media, LLC Proudly Accepts Partnership With MIT's Prestigious Computer Science And Artificial Intelligence ... - May 10th, 2012 [May 10th, 2012]
- Google Driverless Car Ok'd by Nevada - May 10th, 2012 [May 10th, 2012]
- Moving Beyond the Marketing Funnel: Rocket Fuel and Forrester Research Announce Free Webinar - May 10th, 2012 [May 10th, 2012]
- Rocket Fuel Wins 2012 San Francisco Business Times Tech & Innovation Award - May 13th, 2012 [May 13th, 2012]
- Internet Week 2012: Rocket Fuel to Speak at OMMA RTB - May 16th, 2012 [May 16th, 2012]
- How to Get the Most Out of Your Facebook Ads -- Rocket Fuel's VP of Products, Eshwar Belani, to Lead MarketingProfs ... - May 16th, 2012 [May 16th, 2012]
- The Digital Disruptor To Banking Has Just Gone International - May 16th, 2012 [May 16th, 2012]
- Moving Beyond the Marketing Funnel: Rocket Fuel Announce Free Webinar Featuring an Independent Research Firm - May 23rd, 2012 [May 23rd, 2012]
- MASA Showcases Latest Version of MASA SWORD for Homeland Security Markets - May 23rd, 2012 [May 23rd, 2012]
- Bluesky Launches Drones for Aerial Surveying - May 23rd, 2012 [May 23rd, 2012]
- Artificial Intelligence: What happened to the hunt for thinking machines? - May 25th, 2012 [May 25th, 2012]
- Bubble Robots Move Using Lasers [VIDEO] - May 25th, 2012 [May 25th, 2012]
- UHV assistant professors receive $10,000 summer research grants - May 27th, 2012 [May 27th, 2012]
- Artificial intelligence: science fiction or simply science? - May 28th, 2012 [May 28th, 2012]
- Exetel taps artificial intelligence - May 29th, 2012 [May 29th, 2012]
- Software offers brain on the rain - May 29th, 2012 [May 29th, 2012]
- New Dean of Science has high hopes for his faculty - May 30th, 2012 [May 30th, 2012]
- Cognitive Code Announces "Silvia For Android" App - May 31st, 2012 [May 31st, 2012]
- A Rat is Smarter Than Google - June 5th, 2012 [June 5th, 2012]